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Towards a Code of Ethics for Artificial Intelligence
The author investigates how to produce realistic and workable ethical codes or regulations in this rapidly developing field to address the immediate and realistic longer-term issues facing us. She spells out the key ethical debates concisely, exposing all sides of the arguments, and addresses how codes of ethics or other regulations might feasibly be developed, looking for pitfalls and opportunities, drawing on lessons learned in other fields, and explaining key points of professional ethics. The book provides a useful resource for those aiming to address the ethical challenges of AI research in meaningful and practical ways.
Ethical Challenges in Genomics Research
New developments in science and technology have resulted in shifting ethical challenges in many areas including in genomics research. This book enables those who are involved in genomics research, whether as researcher, participant or policy maker, to understand the ethical issues currently developing in this field and to participate actively in these important debates. A clear account is given of how science and technology are outstripping the capacity of previous ethical regulations to cope with current issues, together with practical illustrations of possible ways forward. Key ethical ideas are presented, drawing on the history of research regulation and on an account of the particular ch...
The Oxford Handbook of Ethics of AI
This interdisciplinary and international handbook captures and shapes much needed reflection on normative frameworks for the production, application, and use of artificial intelligence in all spheres of individual, commercial, social, and public life.
AI Ethics
This book introduces readers to critical ethical concerns in the development and use of artificial intelligence. Offering clear and accessible information on central concepts and debates in AI ethics, it explores how related problems are now forcing us to address fundamental, age-old questions about human life, value, and meaning. In addition, the book shows how foundational and theoretical issues relate to concrete controversies, with an emphasis on understanding how ethical questions play out in practice. All topics are explored in depth, with clear explanations of relevant debates in ethics and philosophy, drawing on both historical and current sources. Questions in AI ethics are explored...
The Myth of the Moral Brain
An argument that moral functioning is immeasurably complex, mediated by biology but not determined by it. Throughout history, humanity has been seen as being in need of improvement, most pressingly in need of moral improvement. Today, in what has been called the beginnings of “the golden age of neuroscience,” laboratory findings claim to offer insights into how the brain “does” morality, even suggesting that it is possible to make people more moral by manipulating their biology. Can “moral bioenhancement”—using technological or pharmaceutical means to boost the morally desirable and remove the morally problematic—bring about a morally improved humanity? In The Myth of the Mor...
The Ethics of Biomedical Big Data
This book presents cutting edge research on the new ethical challenges posed by biomedical Big Data technologies and practices. ‘Biomedical Big Data’ refers to the analysis of aggregated, very large datasets to improve medical knowledge and clinical care. The book describes the ethical problems posed by aggregation of biomedical datasets and re-use/re-purposing of data, in areas such as privacy, consent, professionalism, power relationships, and ethical governance of Big Data platforms. Approaches and methods are discussed that can be used to address these problems to achieve the appropriate balance between the social goods of biomedical Big Data research and the safety and privacy of individuals. Seventeen original contributions analyse the ethical, social and related policy implications of the analysis and curation of biomedical Big Data, written by leading experts in the areas of biomedical research, medical and technology ethics, privacy, governance and data protection. The book advances our understanding of the ethical conundrums posed by biomedical Big Data, and shows how practitioners and policy-makers can address these issues going forward.
Sparse Optimization Theory and Methods
Seeking sparse solutions of underdetermined linear systems is required in many areas of engineering and science such as signal and image processing. The efficient sparse representation becomes central in various big or high-dimensional data processing, yielding fruitful theoretical and realistic results in these fields. The mathematical optimization plays a fundamentally important role in the development of these results and acts as the mainstream numerical algorithms for the sparsity-seeking problems arising from big-data processing, compressed sensing, statistical learning, computer vision, and so on. This has attracted the interest of many researchers at the interface of engineering, math...
Genetic Counselling
Contributions to this study are drawn both from health professionals engaged in genetic counselling and from observers and critics with backgrounds in law, philosophy, biology, and the social sciences. This diversity will enable health professonals to examine their activities with a fresh eye, and will help the observer-critic to understand the ethical problems that arise in genetic counselling practice, rather than in imaginary encounters. Most examinations of the ethical issues raised by genetics are concerned in a broad sense with the application of new technology to human reproduction. This volume focuses on genetic counselling and screening as such, providing valuable insights for the health professional, social scientist, philosopher, lawyer, and bioethicist.
Bayesian Epistemology
Probabilistic models have much to offer to philosophy. We continually receive information from a variety of sources: from our senses, from witnesses, from scientific instruments. When considering whether we should believe this information, we assess whether the sources are independent, how reliable they are, and how plausible and coherent the information is. Bovens and Hartmann provide a systematic Bayesian account of these features of reasoning. Simple Bayesian Networks allow us to model alternative assumptions about the nature of the information sources. Measurement of the coherence of information is a controversial matter: arguably, the more coherent a set of information is, the more confident we may be that its content is true, other things being equal. The authors offer a new treatment of coherence which respects this claim and shows its relevance to scientific theory choice. Bovens and Hartmann apply this methodology to a wide range of much discussed issues regarding evidence, testimony, scientific theories, and voting. Bayesian Epistemology is an essential tool for anyone working on probabilistic methods in philosophy, and has broad implications for many other disciplines.
Data Integration
Provides an introduction to the problem of data integration and offers a rigorous account of one of the leading approaches to solving this problem - the relational logic approach. Relational logic provides a theoretical framework for discussing data integration. Moreover, in many important cases, it provides algorithms for solving the problem in a computationally practical way.
